Machine Learning Estimation of the Phase at the Fading Points of an OFDR-Based Distributed Sensor

The paper reports a machine learning approach for estimating the phase in a distributed acoustic sensor implemented using optical frequency domain reflectometry, with enhanced robustness at the fading points. A neural network configuration was trained using a simulated set of optical signals that were modeled after the Rayleigh scattering pattern of a perturbed fiber. Firstly, the performance of the network was verified using another set of numerically generated scattering profiles to compare the achieved accuracy levels with the standard homodyne detection method. Then, the proposed method was tested on real experimental measurements, which indicated a detection improvement of at least 5.1 dB with respect to the standard approach.

Automated summary

The paper presents a machine learning approach to estimate the phase in a distributed acoustic sensor using optical frequency domain reflectometry, with increased robustness at fading points. A neural network configuration is trained using simulated optical signals based on the Rayleigh scattering pattern of a perturbed fiber. The proposed method is compared with the standard homodyne detection method using numerically generated scattering profiles, and it shows an improvement of at least 5.1 dB in detection when tested on real experimental measurements.